Efficient and secure transmission method for image data in the petroleum industry

To address the challenge of efficient and secure transmission of big data in the petroleum industry, this study converts images into three-channel matrix data and performs singular value decomposition (SVD) on these matrices. The method comprehensively considers seven evaluation factors: Laplace operator, grayscale variance function, discrete cosine transform coefficients, image correlation coefficient, entropy function, structural similarity index, and image signal-to-noise ratio. Using the entropy-weighted technique for order preference by similarity to an ideal solution (TOPSIS) method, the decomposed singular values are optimized. A small number of singular values are used to represent the original image, ensuring data authenticity while achieving image compression to reduce data size, and improving transmission efficiency. Additionally, a multi-channel cat face segmentation encryption method is proposed, which performs random segmentation, random encryption, and random shuffling on each color channel of the image separately. This approach addresses limitations in traditional cat face encryption algorithms, such as high linear correlation among color channels and low overall scrambling. Results indicate that the improved singular value compression technique achieves image compression using only 15% of the singular value data while maintaining image clarity. The maximum image compression ratio can reach 4.36, with the average storage space occupied after compression being only 26.29% of the original space. The transmission efficiency in transmission control protocol (TCP) communication increases by an average of 86.39%. Under the condition that the encrypted image achieves zero correlation, the multi-channel cat face segmentation encryption method ensures that pixel values in the encrypted image are completely different across the three color channels, with correlation coefficients of 0.20, 0.22, and 0.25 respectively, reducing the correlation by 0.78, 0.75, and 0.71 compared to traditional cat face encryption methods. This highlights enhanced encryption effectiveness and resistance to decryption. This work provides theoretical and technical support for the digital transformation of the petroleum industry, with a focus on improved data security and transmission efficiency.

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Publication Details

Journal
JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING
Published
2025-03-01
DOI
https://doi.org/10.3724/sp.j.1249.2025.02172
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
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article

Efficient and secure transmission method for image data in the petroleum industry

Shiwen He, Li Zou, Ming Tang, C. L. Philip Chen et al.
JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING
Brain Tumor Detection and Classification
article

Efficient and secure transmission method for image data in the petroleum industry

Shiwen He, Li Zou, Ming Tang, C. L. Philip Chen, Yuanyuan Zhou, Yongxiao Xie
article en

Abstract

To address the challenge of efficient and secure transmission of big data in the petroleum industry, this study converts images into three-channel matrix data and performs singular value decomposition (SVD) on these matrices. The method comprehensively considers seven evaluation factors: Laplace operator, grayscale variance function, discrete cosine transform coefficients, image correlation coefficient, entropy function, structural similarity index, and image signal-to-noise ratio. Using the entropy-weighted technique for order preference by similarity to an ideal solution (TOPSIS) method, the decomposed singular values are optimized. A small number of singular values are used to represent the original image, ensuring data authenticity while achieving image compression to reduce data size, and improving transmission efficiency. Additionally, a multi-channel cat face segmentation encryption method is proposed, which performs random segmentation, random encryption, and random shuffling on each color channel of the image separately. This approach addresses limitations in traditional cat face encryption algorithms, such as high linear correlation among color channels and low overall scrambling. Results indicate that the improved singular value compression technique achieves image compression using only 15% of the singular value data while maintaining image clarity. The maximum image compression ratio can reach 4.36, with the average storage space occupied after compression being only 26.29% of the original space. The transmission efficiency in transmission control protocol (TCP) communication increases by an average of 86.39%. Under the condition that the encrypted image achieves zero correlation, the multi-channel cat face segmentation encryption method ensures that pixel values in the encrypted image are completely different across the three color channels, with correlation coefficients of 0.20, 0.22, and 0.25 respectively, reducing the correlation by 0.78, 0.75, and 0.71 compared to traditional cat face encryption methods. This highlights enhanced encryption effectiveness and resistance to decryption. This work provides theoretical and technical support for the digital transformation of the petroleum industry, with a focus on improved data security and transmission efficiency.

JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERINGVol. 42(2)
Industry, innovation and infrastructure
Openalex Percentile: Top 93%
Brain Tumor Detection and Classification
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